Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Vasanth/bert-stock-sentiment-analyzer")
model = AutoModelForSequenceClassification.from_pretrained("Vasanth/bert-stock-sentiment-analyzer")Quick Links
bert-stock-sentiment-analyzer
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4819
- Accuracy: 0.8604
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5818 | 1.0 | 1250 | 0.4290 | 0.8352 |
| 0.3313 | 2.0 | 2500 | 0.3899 | 0.8568 |
| 0.2029 | 3.0 | 3750 | 0.4819 | 0.8604 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vasanth/bert-stock-sentiment-analyzer")